Method and system for dividing land utilization / cover change time trajectory
By combining the M-K test and sliding t test, the mutation points in the land use/cover change data are identified, and the problem of difficult to divide the time trajectory of land use changes in the prior art is solved, achieving higher accuracy and scientific time Sequence division.
Patent Information
- Application Number
- CN202411751006.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-02
- Publication Date
- 2025-02-07
AI Technical Summary
The prior art is difficult to effectively segment the time trajectory of long-time series during land use/cover changes, resulting in the inability to fully capture dynamic changes within the time period, underestimate the degree of change or miss short-term fluctuations.
Through a combination of M-K test and sliding t test, the test statistics and mutation points in the land use/cover change data are calculated, and the mutation years are identified and screened, and the time trajectory is used as nodes in the time trajectory to achieve more scientific time series division.
It improves the accuracy of the division of the time trajectory of land use/cover changes, captures the integrity of dynamic changes in the time period, has higher detection accuracy, and the divided time series is more scientific.
Smart Images
Figure CN119397451A_ABST
Abstract
Description
Technical Field
[0001] The present invention belongs to the field of land use and planning, and in particular relates to a method and system for dividing a time trajectory of land use / cover change. Background Art
[0002] Land use / cover change is a basic dynamic feature of the Earth system and an important part of global change. Before we delve into the impact of land use / cover change, it is crucial to clarify the characteristics and trends of changes in different periods of time. Social development, policy orientation and natural environmental conditions in different periods of time will have a certain impact on land use / cover change. By scientifically and rationally dividing the time trajectory, we can more accurately analyze and evaluate the evolution of land use / cover and reveal its profound impact on the ecosystem, climate and socio-economy. This division is not only a key step in understanding land use change, but also lays a solid foundation for in-depth exploration of its impact on landscape pattern, climate change and ecosystem services.
[0003] At present, most of the research based on land use / cover change in different fields mainly explores the dynamic changes between different land types based on fixed time intervals or subjective division of years. The division of different time periods in the time trajectory is mainly based on traditional autonomous division, but the traditional division method does not fully consider that the time trajectory nodes of land use dynamic changes are not simply equidistant, which makes it impossible to fully capture the dynamic changes within the time period, leading to underestimation of the degree of change or missing short-term fluctuations. Summary of the invention
[0004] In order to solve the problem of effectively segmenting the time trajectory of long-term series changes in the process of land use / cover change, the present invention provides a method and system for segmenting the time trajectory of land use / cover change.
[0005] In order to achieve the above object, the present invention provides the following technical solutions:
[0006] A method for dividing a time trajectory of land use / cover change includes the following steps:
[0007] Obtain land use / cover change datasets for the study area for many consecutive years;
[0008] Calculate the test statistic of the data in the land use / cover change data set for consecutive years through the MK test, judge the significant trend change of land use types in different years according to the test statistic, calculate the statistic and its reverse sequence under the significant trend change, and obtain the first mutation point set in the long time series trajectory according to the intersection of the statistic and its reverse sequence in the confidence interval;
[0009] Perform mutation detection on the data in the dataset of land use / cover change over consecutive years using a sliding t-test to obtain the second set of mutation points in the long-term sequence trajectory;
[0010] Merge the data of the first set of mutation points and the second set of mutation points, identify and screen the corresponding mutation years based on the merged mutation points, and use the screened mutation years as the nodes of the long-term sequence trajectory to obtain the division result of the land use / cover change time trajectory.
[0011] Preferably, calculate the test statistic of the data in the dataset of land use / cover change over consecutive years through the M-K test, and judge the significant trend change of land use types in different years according to the test statistic. The test statistic is specifically calculated by the following formula:
[0012]
[0013] Among them, S is the statistic for detecting the sample sequence of consecutive years of different land use / cover types; X a and X b correspond to the area values of a certain land use / cover type respectively, and a < b; n represents the total number of years; sgn is the sign function; V ar (S) and Z represent the test variance and the test statistic respectively;
[0014] When the test statistic is positive, it shows an upward trend change; when the test statistic is negative, it shows a downward trend change.
[0015] Preferably, calculate the statistic and its reverse sequence specifically through the following formula:
[0016]
[0017] Among them, S k represents the cumulative number of the order sequence X composed of the time series with n different land use / cover types; a, b, and k represent different years; E (Sk) is the mean of the cumulative number S k ; V ar(Sk) is the variance of the cumulative number S k ; X a and X b correspond to the area values of a certain land use / cover type respectively; r b is the order sequence composed of the time series X b defined as r b of X b > X a and the sample cumulative number with 1 ≤ a < b; UF k is the statistic of the standard normal distribution, UB kFor UF k The reverse sequence of
[0018] If UB k With UF k They intersect within the confidence interval, and the intersection point is the mutation point.
[0019] Preferably, a sliding t-test is used for mutation detection, specifically:
[0020]
[0021] Where n is the statistic of area series of different land use / cover types; X is the time series; is the mean; is the variance; S is the intermediate parameter; t is a sliding t distribution with degrees of freedom V = n1 + n2-2;
[0022] If the t value exceeds the confidence interval, it is considered that a mutation has occurred and the mutation point is obtained.
[0023] Preferably, the land use / cover dataset for multiple consecutive years specifically includes a time series and different land use / cover types.
[0024] The present invention also provides a land use / cover change time trajectory partitioning system, which specifically includes:
[0025] Data acquisition module, used to obtain land use / cover change datasets of the study area for many consecutive years;
[0026] The first data processing module is used to calculate the test statistic of the data in the land use / cover change data set for consecutive years through the MK test, judge the significant trend change of land use types in different years according to the test statistic, calculate the statistic and its reverse sequence under the significant trend change, and obtain the first mutation point set in the long time series trajectory according to the intersection of the statistic and its reverse sequence in the confidence interval;
[0027] A second data processing module is used to perform mutation detection on the data in the land use / cover change dataset for consecutive years using a sliding t-test to obtain a second mutation point set in a long time series trajectory;
[0028] A partitioning module is used to merge the data of the first mutation point set and the second mutation point set, identify and screen the corresponding mutation years according to the merged mutation points, use the screened mutation years as nodes of the long time series trajectory, and obtain the partitioning result of the land use / cover change time trajectory.
[0029] The present invention also provides a computer device, including a memory, a processor and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps described in the method for dividing the time trajectory of land use / cover change.
[0030] The present invention also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is loaded by a processor, the steps described in the method for dividing the time trajectory of land use / cover change can be executed.
[0031] The method for dividing the time trajectory of land use / cover change provided by the present invention has the following beneficial effects:
[0032] The present invention obtains a data set of land use / cover change for many consecutive years, and obtains mutation points in the long-term series of land use / cover change through the MK test and the sliding t test. The MK test can detect the change trend and mutation points of the long-term series under different land use / cover change types, and the sliding t test can accurately identify the local change trend and locate the location where the mutation point occurs. The mutation points detected by the MK test and the sliding t test are combined, the mutation year is identified, and the division result of the time trajectory of land use / cover change is obtained, which captures the dynamic changes within the time period more completely, has higher detection accuracy, and the divided time series is more scientific. The conventional method of dividing the time trajectory at fixed time intervals in the relevant research on the time trajectory of land use / cover change is changed, which is conducive to more accurate analysis and evaluation of the evolution process of land use / cover types. BRIEF DESCRIPTION OF THE DRAWINGS
[0033] In order to more clearly illustrate the embodiment of the present invention and its design scheme, the following briefly introduces the drawings required for this embodiment. The drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.
[0034] Figure 1 The present invention is a flow chart of a method for dividing the time trajectory of land use / cover change.
[0035] Figure 2 It is the mutation detection rate identified by different methods in the embodiments of the present invention.
[0036] Figure 3 This is the time series segmentation and extreme value rate of the area changes of different land use / cover types in Qilian Mountain National Park from 1990 to 2022 based on the traditional method (with 5 years and 10 years as time nodes) in an embodiment of the present invention. Figure 3In the figure, (a)-(g) represent the land use / cover type areas and their changing trends of farmland, forest, shrub, grassland, water area, ice and snow, and bare land, respectively. The solid and hollow circles represent the extreme points divided by 5 and 10 years, respectively; (h) represents the extreme rate of each land use / cover type.
[0037] Figure 4 It is a result diagram of the MK test and the sliding t test combined with the mutation point and MK trend change test in an embodiment of the present invention.
[0038] Figure 5 The technical flow chart of screening mutation points according to an embodiment of the present invention.
[0039] Figure 6 Figure 3 is the area change trend and mutation frequency of different land use / cover types in Qilian Mountain National Park from 1990 to 2022 based on the MK test method in an embodiment of the present invention, where (a)-(g) represent the change trends of farmland, forest, shrub, grassland, water area, ice and snow, and bare land, respectively, the dark trend and light trend represent UF and UB sequences, respectively, and (h) represents the mutation frequency of each land use / cover type.
[0040] Figure 7 These are the mutation points and mutation frequencies of the areas of different land use / cover types in Qilian Mountain National Park from 1990 to 2022 based on the sliding t-test method in an embodiment of the present invention, where (a)-(g) represent the time series mutation points of farmland, forest, shrub, grassland, water area, ice and snow, and bare land, respectively, and (h) represents the mutation frequency of each land use / cover type. DETAILED DESCRIPTION
[0041] In order to enable those skilled in the art to better understand the technical solution of the present invention and implement it, the present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and cannot be used to limit the scope of protection of the present invention.
[0042] Example
[0043] The present invention provides a method for dividing the time trajectory of land use / cover change, taking the land use / cover of Qilian Mountain National Park in China from 1990 to 2022 as an example. Figure 5 As shown, the specific steps include:
[0044] S1. Obtain the land use / cover datasets for consecutive years. The China Land Cover Dataset (CLCD) with a resolution of 30m is adopted. This dataset shows good consistency when compared with various land cover thematic products, and the overall accuracy reaches 80%. When using a resolution of 1km, there are 7 land use / cover types in Qilian Mountains National Park, including farmland, ice and snow, shrubs, forests, grasslands, water areas, and bare land.
[0045] S2. Calculate the test statistic of the data in the land use / cover datasets for consecutive years through the Mann-Kendall (M-K) test, and judge the significant trend changes of land use types in different years according to the test statistic.
[0046] Calculate the Z value of the test statistic. If |Z| > 1.96, it indicates significance, and the positive or negative sign represents an upward or downward trend. The specific calculation formula is:
[0047]
[0048] where S is the statistic for detecting the sample sequence of different land use / cover types for consecutive years; X a and X b correspond to the area values of a certain land use / cover type respectively, and a < b, n represents the year, n = 33; sgn is the sign function; V ar (S) and Z represent the test variance and the test statistic respectively.
[0049] Under the significant trend change, calculate the statistic and its reverse sequence, and obtain the first set of mutation points in the long-time series trajectory according to the intersection points of the statistic and its reverse sequence within the confidence interval.
[0050]
[0051]
[0052] where S k represents the cumulative number of the ordered sequence X composed of the time series with n different land use / cover types; a, b, and k represent different years; is the mean of the cumulative number S k ; is the variance of the cumulative number S k ; X a and X b correspond to the area values of a certain land use / cover type respectively; r b is the ordered sequence defined by the time series X b and is defined as r b of X b > X aThe cumulative number of samples with 1≤a<b; UF k is the statistic of the standard normal distribution, UB k For UF k The reverse sequence of
[0053] If UB k With UF k They intersect within the confidence interval, and the intersection point is the mutation point.
[0054] S3. Use the sliding t-test to perform mutation detection on the data in the land use / cover dataset for consecutive years to obtain the second set of mutation points in the long time series trajectory.
[0055] The sliding t test is used for mutation detection. If the t value exceeds the confidence interval, it is considered that a mutation has occurred. The specific formula is as follows:
[0056]
[0057] Where n is the statistic of the area series of different land use / cover types; X is the time series, X = 33; is the mean; is the variance; S is the intermediate parameter; t is the sliding t distribution with degrees of freedom V = n1 + n2-2.
[0058] like Figure 2 are the mutation rates in different methods, representing the mutation detection rates of MK mutation test, sliding t test and the combination of the two.
[0059] S4. The first mutation point set is merged with the second mutation point set. The corresponding mutation year is identified and selected according to the merged mutation points as the node of the long time series trajectory, and the division result of the land use / cover change time trajectory is obtained, such as Figure 4 shown.
[0060] By combining the MK test and the sliding t test, multiple mutation points were identified, and it was found that the detection accuracy of years with more than or equal to 4 mutations (such as 1997, 2008, and 2019) was significantly improved, especially the detection accuracy of the sliding t test reached 71.43%. It further illustrates that high-frequency mutation points represent more stable changes that are consistently identified by multiple methods. Therefore, selecting years with more than or equal to 4 mutations as nodes can better reflect the true situation of land use changes and reduce the impact of accidental mutations or detection errors. Based on this, we summarize and propose a land use / cover change time trajectory segmentation method based on the combination of MK and sliding t to achieve scientific division of long time series.
[0061] Figure 6The area change trend and mutation frequency of different land use / cover types in Qilian Mountain National Park from 1990 to 2022 based on the MK test method in the embodiment of the present invention; Figure 7 These are the mutation points and mutation frequencies of the areas of different land use / cover types in Qilian Mountain National Park from 1990 to 2022 based on the sliding t-test method in an embodiment of the present invention.
[0062] Based on the traditional method, different land use / cover types are divided into 5-year or 10-year intervals, and the time series segmentation and extreme value rate are obtained, such as Figure 3 As shown. Different land use / cover types are divided with 5-year intervals as time trajectory nodes. It can be found that from 1990 to 1995, the areas of the other 6 land use / cover types except forest all had extreme values in 1995 or around; from 1995 to 2000, the areas of the 7 land use / cover types all had multiple extreme values, and there were obvious extreme values in 1997 or around; from 2000 to 2005, the areas of farmland, grassland and water area all had extreme values in 2005 or around; from 2005 to 2010, the areas of all land use / cover types had extreme values in 2008 or around; from 2010 to 2015, except for the extreme value of ice and snow in 2013, the areas of the other 6 types had no obvious extreme values; from 2015 to 2020, farmland, grassland, water area, ice and snow and bare land all had obvious extreme values in 2019 and around. In general, the extreme value existence rate of shrubs and grasslands is 50.00%, the extreme value existence rate of grasslands and bare land is 33.33%, and the overall extreme value existence rate is only 28.57%. The overall extreme value existence rate is only 14.29% when the time trajectory nodes are divided into 10-year intervals, and it is impossible to identify multiple extreme value points in the area of different land use / cover types. Figure 3 shown.
[0063] Long time series are mainly used to describe the dynamic changes of different application scenarios in different periods, including precipitation, temperature, runoff, well depth monitoring, sediment load, etc. The introduction of mutation point detection can effectively identify the time and year when mutations occur in various scenarios, providing reliable data support for subsequent corresponding research. The non-parametric Mann Kendall (MK) test method is used to detect the mutation year, which is suitable for data of various time series and can effectively identify mutation points and long-term trends; while the sliding t test used for parameters can accurately identify local change trends and locate the location of mutation points. The combination of the two can improve the accuracy of mutation point detection.
[0064] The present invention also provides a land use / cover change time trajectory partitioning system, which specifically includes:
[0065] The data acquisition module is used to obtain the land use / cover change dataset of the study area for many consecutive years.
[0066] The first data processing module is used to calculate the test statistic of the data in the land use / cover change data set for consecutive years through the MK test, judge the significant trend changes of land use types in different years according to the test statistic, calculate the statistic and its inverse sequence under the significant trend change, and obtain the first mutation point set in the long time series trajectory according to the intersection of the statistic and its inverse sequence in the confidence interval.
[0067] The second data processing module is used to perform mutation detection on the data in the land use / cover change data set for consecutive years using a sliding t-test to obtain a second mutation point set in the long time series trajectory.
[0068] A partitioning module is used to merge the data of the first mutation point set and the second mutation point set, identify and filter the corresponding mutation year according to the merged mutation point, use the filtered mutation year as the node of the long time series trajectory, and obtain the partition result of the land use / cover change time trajectory. The present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory, and the processor executes the computer program to implement the steps in an embodiment of a method for partitioning the time trajectory of land use / cover change. The specific implementation method can be found in the method embodiment, which will not be repeated here.
[0069] Furthermore, the present invention also provides a non-temporary computer-readable storage medium containing instructions, and a computer program is stored on the storage medium. For example, a memory containing instructions, the above instructions can be executed by a processor of a computer device to complete the above method. For example, a non-temporary computer-readable storage medium can be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, and an optical data storage device. When the computer program is executed by the processor, it can implement the steps in an embodiment of a method for dividing the time trajectory of land use / cover change. The specific implementation method can be found in the method embodiment, which will not be repeated here.
[0070] It will be appreciated by those skilled in the art that embodiments of the present invention may provide methods, systems or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.
[0071] The present invention is described by flowcharts and / or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.
[0072] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.
[0073] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.
[0074] It should be pointed out that the specific implementation methods described above can enable those skilled in the art to understand the invention more comprehensively, but do not limit the invention in any way. Therefore, although the invention has been described in detail in this specification and embodiments, those skilled in the art should understand that the invention can still be modified or replaced by equivalents; and all technical solutions and improvements that do not deviate from the spirit and scope of the invention are included in the protection scope of the patent for the invention. Any figure mark in the claims should not be regarded as limiting the claims involved. Any simple change or equivalent replacement of the technical solution that can be obviously obtained by any technician familiar with the field within the technical scope disclosed in the present invention belongs to the protection scope of the present invention.
Claims
1. A method for dividing the time trajectory of land use / cover change, characterized in that: The following steps are involved: Obtain land use / cover change datasets for the study area for many consecutive years; Calculate the test statistic of the data in the land use / cover change data set for consecutive years through the MK test, judge the significant trend change of land use types in different years according to the test statistic, calculate the statistic and its reverse sequence under the significant trend change, and obtain the first mutation point set in the long time series trajectory according to the intersection of the statistic and its reverse sequence in the confidence interval; Using a sliding t-test to perform mutation detection on the data in the land use / cover change dataset for consecutive years, to obtain a second mutation point set in the long time series trajectory; The data of the first mutation point set and the second mutation point set are merged, and the corresponding mutation years are identified and screened according to the merged mutation points. The screened mutation years are used as nodes of the long time series trajectory to obtain the division results of the land use / cover change time trajectory.
2. A method for dividing the time trajectory of land use / cover change according to claim 1, characterized in that: The test statistic of the data in the land use / cover change data set for consecutive years is calculated by the MK test, and the significant trend changes of land use types in different years are judged according to the test statistic, wherein the test statistic is specifically calculated by the following formula: Among them, S is the statistic of the sample sequence that has continuously detected different land use / cover types for many years; X a and X b correspond to the area values of a certain land use / cover type respectively, and a < b; n represents the total number of years; sgn is the sign function; V ar (S) and Z represent the test variance and the test statistic respectively; When the test statistic is a positive value, it shows an upward trend; when the test statistic is a negative value, it shows a downward trend.
3. The method for dividing the time trajectory of land use / cover change according to claim 1, characterized in that: The calculation statistic and its reverse sequence are specifically calculated by the following formula: Among them, S k represents the cumulative number of the ordered series X consisting of n different land use / cover types; a, b and k represent different years; E (Sk) is the cumulative number S k The mean value of V ar(Sk) is the cumulative number S k The variance of X a With X b The area value corresponding to a certain land use / cover type; r b For the time series X b The order sequence is defined as r b X b >X a The cumulative number of samples with 1≤a<b; UF k is the statistic of the standard normal distribution, UB k For UF k The reverse sequence of The UB k With UF k The intersection point where the sequences intersect within the confidence interval is the mutation point.
4. The method for dividing the time trajectory of land use / cover change according to claim 1, characterized in that: The sliding t-test was used for mutation detection, specifically: Where n is the statistic of area series of different land use / cover types; X is the time series; is the mean; is the variance; S is the intermediate parameter; t is a sliding t distribution with degrees of freedom V = n1 + n2-2; The calculated t value is used to determine whether it exceeds the confidence interval. If it exceeds the confidence interval, it is considered that a mutation has occurred and the mutation point is obtained.
5. The method for dividing the time trajectory of land use / cover change according to claim 1, characterized in that: The land use / cover change dataset for consecutive years specifically includes time series and different land use / cover types.
6. A land use / cover change time trajectory partitioning system, characterized in that: include: Data acquisition module, used to obtain land use / cover change datasets of the study area for many consecutive years; The first data processing module is used to calculate the test statistic of the data in the land use / cover change data set for consecutive years through the MK test, judge the significant trend change of land use types in different years according to the test statistic, calculate the statistic and its reverse sequence under the significant trend change, and obtain the first mutation point set in the long time series trajectory according to the intersection of the statistic and its reverse sequence in the confidence interval; A second data processing module is used to perform mutation detection on the data in the land use / cover change dataset for consecutive years using a sliding t-test to obtain a second mutation point set in a long time series trajectory; A partitioning module is used to merge the data of the first mutation point set and the second mutation point set, identify and screen the corresponding mutation years according to the merged mutation points, use the screened mutation years as nodes of the long time series trajectory, and obtain the partitioning result of the land use / cover change time trajectory.
7. A computer device comprising a memory, a processor and a computer program stored in the memory, characterized in that: The processor executes the computer program to implement the steps of the method according to any one of claims 1 to 5.
8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is loaded into a processor, it can execute the steps of the method according to any one of claims 1 to 5.